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PubMed1990

Receptor interactions in modulating ventilatory activity.

The ventilatory control system utilizes a variety of sensory receptor groups, including chemoreceptors and mechanoreceptors, to provide feedback concerning the status of controlled variables. Most ventilatory responses to altered receptor inputs generally involve a complex interaction between several receptor groups, central integrative mechanisms, and other modulatory inputs (e.g., "state," hormonal, or neurotransmitter status). Because the control system is complex, nonlinear, and dynamic, the ultimate ventilatory response elicited by a given stimulus is not easy to predict based on the reflex effects of individual receptor groups studied in isolation. A full understanding of the role that sensory receptors play in ventilatory control requires information concerning interactions among receptor groups and with other elements of the control system. The complexity of the problem and the lack of a uniform definition of the term "interaction" has hindered research in this area. An interaction is defined as a nonadditive relationship between independent inputs to the system. Within this definition, five domains of interaction are described. 1) Algebraic interactions occur in ventilation and/or its components because of their multiplicative and nonlinear relationship. 2) Closed-loop interactions occur because of the prevalence of feedback loops within the respiratory control system. 3) Neural interactions reflect central nervous system integration of simultaneous receptor inputs and are demonstrated when feedback loops are opened. Three subdomains of neural interactions are defined: modulatory, dynamic, and range-specific neural interactions. 4) Mechanical interactions result from nonlinear transformations of motoneuron output into mechanical actions. 5) Adaptive interactions occur when paired receptor or modulatory inputs alter future responses. To understand the role of any sensory receptor group in ventilatory control, it is necessary to define its interactions with other control system elements in each of these domains. Understanding the mechanisms of these interactions requires detailed information about the physical system subserving ventilatory control (mechanics and gas exchange) and the relevant properties of the neural network coordinating their actions.

Adaptation, Physiological↗
PubMed1996

Cross-correlation analysis reveals laminar differences in thalamocortical interactions in the somatosensory system.

1. Spontaneous and stimulus-induced activity were recorded from corresponding somatotopic representations in the ventroposterolateral nucleus (VPL) of the thalamus and primary somatosensory (SI) cortex of intact, halothane-anesthetized cats. Thalamic and cortical neurons with overlapping receptive fields on the hairy skin of the forelimb were excited by a series of interleaved air jets aimed at multiple skin sites. 2. The laminar locations of 68% (240 of 355) of the neurons recorded in SI cortex were histologically reconstructed and responses of these 240 SI neurons were analyzed with respect to responses recorded from 118 thalamic neurons. Maximum responsiveness during the initial onset (1st 100 ms) of air jet stimulation was similar for neurons distributed throughout all layers of SI cortex (2-4 spikes per stimulus) and did not differ significantly from VPL responses. During the subsequent plateau phase of the stimulus, VPL neurons discharged at a mean rate of 19.0 spikes/ s and neurons in cortical layers II, IIIa, IIIb, and IV discharged at similar rates. Mean responsiveness during the plateau phase of the stimulus was significantly reduced among neurons in cortical layers V and VI and only averaged 7.1 and 3.9 spikes/s, respectively. 3. Responses recorded simultaneously from pairs of thalamic and cortical neurons were analyzed with cross-correlation analysis to determine differences in the incidence and strength of neuronal interactions as a function of cortical layer. Among 421 thalamocortical neuron pairs displaying stimulus-induced responses, 68 neuron pairs exhibited significant interactions during air jet stimulation. A laminar analysis revealed that 28% (45 of 163) of the neurons in the middle cortical layers displayed significant interactions with thalamic neurons, whereas only 14% (13 of 92) of superficial layer neurons and 6% (10 of 166) of deep layer neurons were synchronized with thalamic activity during air jet stimulation. When thalamocortical efficacy for different layers of cortex was plotted as a cumulative frequency distribution, the strongest interactions in the middle cortical layers were twice as strong as interactions involving the superficial or deep cortical layers. 4. More than 70% of stimulus-induced interactions involved thalamic discharges followed by subsequent cortical discharges and the majority of these interactions involved interspike intervals of < or = 3 ms. Nearly 75% (27 of 37) of interactions in the thalamocortical direction that involved cortical neurons in layers IIIb and IV transpired within a 3-ms interspike interval. For interactions with superficial or deep cortical layers, the proportion of thalamocortical interactions transpiring within 3 ms was only 58% (7 of 12) and 33% (2 of 6), respectively. 5. Cross-correlation analysis of spontaneous activity indicated that 124 pairs of thalamic and cortical neurons displayed synchronous activity in the absence of sensory stimulation. A laminar analysis indicated that similar proportions of cortical neurons in each layer were synchronized with thalamic activity in the absence of cutaneous stimulation. Thus 27% (44 of 163) of middle layer neurons, 30% (28 of 92) of superficial layer neurons, and 31% (51 of 166) of deep layer neurons displayed spontaneous interactions with thalamic neurons. The temporal pattern of spontaneous activity was examined with autocorrelation analysis to determine whether neuronal oscillations were essential for coordinating thalamic and cortical activity in the absence of peripheral stimulation. Only 18.5% (23 of 124) of spontaneous interactions between thalamic and cortical neurons were associated with periodic activity, which suggests that thalamocortical synchronization occurs before the constituent neurons begin to oscillate. 6. The influence of sensory stimulation on spontaneous interactions was examined in 31 pairs of thalamic and cortical neurons that exhibited interactions during prestimulus and stimulus in

Animals↗
PubMed1996

Mechanical actions of heterogenic reflexes among ankle stabilizers and their interactions with plantarflexors of the cat hindlimb.

1. The stretch-evoked reflex organization of muscles whose major action is to abduct [peroneus brevis (PB); peroneus longus (PL)] and adduct [tibialis posterior (TP); flexor digitorum longus (FDL); flexor hallucis longus (FHL)] the ankle, and their interactions with the hindlimb extensors gastrocnemius (G) and soleus (S), were studied in 27 unanesthetized decerebrate cats. Ramp-hold-release stretches of physiological amplitudes were applied to muscle tendons detached from their bony insertion, and muscle force output was measured in response to these perturbations. Flexion and crossed-extension reflexes were used to modulate baseline force. 2. PB and TP shared strong, length-dependent, short-latency inhibitory reflexes prominent when the muscles were either actively generating force or quiescent. The mechanical characteristics of this reflex suggest Ia reciprocal inhibition as the underlying mechanism. Just as reciprocal inhibition between S and tibialis anterior stiffens the ankle joint against sagittal perturbations, we propose that reciprocal inhibition between PB and TP stiffens the ankle joint against nonsagittal perturbations. 3. In all preparations (n = 7) and under all conditions examined, PB and PL shared well-demonstrated mutual excitation. The reflex responses were asymmetric (favoring excitation of PL), length dependent, and occurred simultaneously with the stretch reflex at a latency of 16-18 ms. Mutual monosynaptic projections previously described between these two muscles explain all of the above findings. Our data further demonstrate that, under certain conditions, the ensemble activity of this reflex interaction has a powerful effect on the mechanical behavior of the muscle. 4. The heterogenic reflex organization of the ankle adductors was as follows: FDL evoked a modest excitation on TP, whereas FHL evoked weak inhibition. Latency of the excitation from FDL onto TP (24 ms) was greater than expected if the reflex were mediated by heteronymous Ia afferents. In all preparations examined (n = 3), TP contributed no significant reflexes onto either FDL or FHL. 5. Mutual, asymmetric inhibition characterized interactions between PB and the plantarflexors S and G. Most remarkable was a novel, long-latency (72-74 ms) reflex inhibition evoked on both S and G by stretch of PB. When this inhibition occurred, it dramatically decreased the S (or G) stretch response. Longer PB lengths evoked greater inhibition of isometric S; regression analysis indicated that the model best predicting this inhibition contained muscle force and stiffness terms. No long-latency reflexes were noted from either G or S onto PB. The mechanism underlying long-latency inhibition is presently unknown; however, features of this interaction suggest interneurons receive either group II or group III afferent input. 6. G and TP shared short latency, mutually inhibitory, asymmetric reflexes favoring inhibition of TP. No long-latency interactions were noted, nor were there any mechanically significant interactions between S and TP. 7. Reflex interactions across the abduction/adduction axis thus favored inhibition of plantarflexion and adduction torques while emphasizing abduction torques: PB/S (or PB/G) interactions were mutual, asymmetric, and favored inhibition of G and S; TP/G interactions were mutual, asymmetric, and favored inhibition of TP; TP/PB interactions were approximately balanced. The overall mechanical outcome of these inhibitory interactions may partly underlie the global corrective strategy seen in intact cats subjected to linear perturbations. 8. No significant reflex interactions were demonstrated between PL and TP, G, or S, nor were any long-latency reflexes noted. Thus, whereas reflex interactions between the stereotypically activated PB and other stereotypically activated muscles (including TP, G, and S) were strong and well-demonstrated, interactions between the variably activated PL and these same muscles were far weaker.

Animals↗
PubMed1991

Calculation of solvation interaction energies for protein adsorption on polymer surfaces.

Of the interactions that govern protein adsorption on polymer surfaces, solvation interactions (repulsive hydration and attractive hydrophobic interactions) are thought to be among the most important. The solvation interactions in protein adsorption, however, have not been dealt with in theoretical calculation of the adsorption energy owing to the difficulties in modelling such interactions. We have evaluated the solvation interaction energies using the fragment constant method of calculating the partition coefficients of amino acids. The fundamental assumption of this approach is that the partition coefficients of amino acids between water and organic solvent phases are related to the free energies of transfer from bulk water to the polymer surface. The X-ray crystallographic protein structures of lysozyme, trypsin, immunoglobulin Fab, and hemoglobin from the Brookhaven Protein Data Bank were used. The model polymer surfaces were polystyrene, polypropylene, polyethylene, poly(hydroxyethyl methacrylate) [poly(HEMA)], and poly(vinyl alcohol). All possible adsorption orientations of the proteins were simulated to study the effect of protein orientation on the solvation interactions. Protein adsorption on either hydrophobic or hydrophilic polymer surfaces was examined by considering the sum of solvation and other interaction energies. The results showed that the contribution of the solvation interaction to the total protein adsorption energy was significant. The average solvation interaction energy ranged from -259.1 to -74.1 kJ/mol for the four proteins on the hydrophobic polymer surfaces, such as polystyrene, polypropylene, and polyethylene. On the other hand, the average solvation interaction energies on hydrophilic surfaces such as poly(HEMA) and poly(vinyl alcohol) were larger than zero. This indicates that repulsive hydration interactions are in effect for protein adsorption on hydrophilic polymer surfaces. The total interaction energies of the proteins with hydrophobic surfaces were always lower than those with more hydrophilic surfaces. This trend is in agreement with the experimental observations in the literature. This study suggests that consideration of the solvation interaction energies is necessary for accurate calculation of the protein adsorption energies.

Adsorption↗
PubMed2003

Isoform-selective interactions between estrogen receptors and steroid receptor coactivators promoted by estradiol and ErbB-2 signaling in living cells.

Estrogen receptor (ER)alpha and -beta interact with a variety of coactivator proteins, most notably members of the steroid receptor coactivator (SRC) family, and these interactions have been shown to be regulated by estrogenic ligands and growth factor signaling. Here, using fluorescence resonance energy transfer (FRET), the selectivity of different stimulants on ERalpha and -beta interactions with coactivator receptor interaction domains (RIDs) were examined in living cells. We first show that ERalpha and ERbeta homo- and heterodimers form in vivo independently of the presence of 17beta-estradiol (E2) or antiestrogens. We then demonstrate that E2 enhances interactions between ERalpha and the RIDs of SRC-1 and SRC-3, whereas the interaction between ERalpha with the SRC-2 RID is ligand independent. The transcriptionally inactive mutant ERalphaL539A showed no interaction with all three SRC RIDs. Similarly, treatment with the antagonists 4-hydroxytamoxifen and EM-652 abolished all interactions between ERalpha and the SRC RIDs. FRET data also demonstrate that, in contrast to ERalpha, ERbeta interacts with all three SRC RIDs in a ligand-independent manner. However, these interactions were further enhanced or stabilized by E2, whereas the antiestrogen EM-652 abolished all interactions. In the presence of both ERalpha and ERbeta, E2 treatment led to the recruitment of SRC RIDs to the nuclei. Finally, expression of the oncogenic activated ErbB-2/Neu protein specifically enhanced ERalpha but not ERbeta interactions with SRC RIDs to an extent similar to E2-stimulated interactions. In summary, using FRET, we demonstrated preferential interactions between ER isoforms and coactivators upon hormonal treatment and activation of a growth factor signal transduction pathway in living cells.

Acetyltransferases↗
PubMed2006

Protein-protein interactions more conserved within species than across species.

Experimental high-throughput studies of protein-protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein-protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein-protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein-protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/

Animals↗
PubMed2006

Potential for drug interactions in seniors with osteoarthritis.

OBJECTIVE: To document the potential for drug interactions in seniors with osteoarthritis and to consider the usefulness of computerized support for detecting clinically important interactions. DESIGN: Self-administered mailed survey. One question requested a list of all medications (prescribed drugs and self-care products, including herbal and "natural" health products) taken in the last 7 days. Interactions among all medications were assessed using an on-line software package. SETTING: Three urban primary care practices in Nova Scotia. PARTICIPANTS: Questionnaires were sent to 244 patients aged 65 years and older with physician-confirmed osteoarthritis. MAIN OUTCOME MEASURES: Number of potential interactions and level of clinical significance associated with each. RESULTS: Response rate was 78% (n = 191); 174 respondents (92%) supplied information on medications. Respondents took an average of 4.7 products of which 2.8 were prescription medications and 1.9 were self-care products. A total of 214 potential interactions were identified; 30 (14%) of these were clinically significant. Most interactions involved nonprescription products, most frequently acetylsalicylic acid. Recommendations in 29 of these 30 clinically significant interactions were cautionary, advising such measures as closer monitoring of blood tests, observation for toxic effects, or making patients aware of side effects. Only 1 interaction prompted a recommendation for avoidance. Respondents reported use of 7 different herbal and natural health products; these products were associated with 5 clinically insignificant interactions. CONCLUSION: Risk of drug interactions in seniors might be high, but few interactions are clinically significant. Only 1 found in our study carried a recommendation for avoidance. The on-line program reported all significant interactions, but the high proportion of insignificant interactions (6 : 1) also reported could lead physicians to override computer-generated alerts.

Aged↗
PubMed2004

A longitudinal analysis of antihypertensive drug interactions in a Medicaid population.

BACKGROUND: Drug interactions are a frequent cause of adverse drug events. We evaluated whether the frequency of previously reported antihypertensive drug-drug interactions could be reduced by pharmaceutical case management. METHODS: Patients >30 years of age with hypertension who were enrolled in the Iowa Pharmaceutical Case Management (PCM) program were evaluated. All prescription claims for patients were obtained on their date of eligibility and again 9 months later. A drug interaction database was developed to examine potential drug interactions in each patient's regimen. RESULTS: Antihypertensive drugs were taken by 1377 patients at baseline and at 9-month follow-up. Highly significant antihypertensive drug interactions were observed at baseline in 35% of patients (0.47 per patient), and interaction prevalence did not change over time. Decreases in the number of drug interactions tended to occur more commonly among patients of pharmacies that provided the highest intensity of service (11.5% in high-intensity pharmacies v 9% in low- or zero-intensity pharmacies, but this did not achieve statistical significance). Nearly 75% of patients had an interaction of any significance level, and the total number of interactions increased over time (P =.0067). CONCLUSIONS: This Medicaid population with hypertension had a very high prevalence of potential drug interactions. The prevalence of interactions did not change, but the mean number of all interactions actually increased over time. There was some suggestion that higher-intensity pharmacies might be more successful in minimizing the risk of clinically significant drug interactions when compared with lower-intensity pharmacies.

Adrenergic beta-Antagonists↗
PubMed2006

Molecular interactions between estrogen receptor and its ligand studied by the ab initio fragment molecular orbital method.

The ab initio fragment molecular orbital calculations were performed for molecular interactions of the whole estrogen receptor (ER) ligand-binding domain with a natural ligand, 17beta-estradiol (EST). The interaction energies of the ligand at the residue level were calculated using HF and MP2 methods with several basis sets. The charge-transfer (CT) interactions were also analyzed based on configuration analysis for fragment interaction. Strong electrostatic interactions were observed between the EST and surrounding charged/polarized residues, Glu353, Arg394, His524, and Thr347. Weak electrostatic and significant van der Waals dispersion interactions were observed between the EST and the many surrounding hydrophobic residues. Together with the experimental interpretations, both interactions equally contributed to the total binding energies, and it was found that the inclusion of electron correlation was essential to obtain an appropriate picture of the interaction. The strongest interaction energy was observed between Glu353 and the EST, and the CT interactions from the lone-pair orbital of the carbonyl oxygen of Glu353 to the sigma(OmicronEta) orbital of the hydroxyl group of EST were found to be important. The CT interactions from the lone-pair orbital of EST to the sigma(NuEta) of Arg394 and from the lone-pair orbital of EST to the sigma(NuEta) of His524 were also observed. These CT interactions occurred through the hydrogen-bond networks between the ER and EST. Therefore, electron donations from the ER to the EST and electron back-donations from EST to the ER were characteristic of ER-ligand binding. Our approach provides a powerful tool to understanding detailed molecular interactions at the quantum mechanical level.

Estradiol↗
PubMed1999

Pharmacist recognition of potential drug interactions.

The ability of pharmacists to identify potential drug interactions was studied. Simulated medication profiles were created from a list of 16 drugs. Staff pharmacists and soon-to-graduate student pharmacists at a Veterans Affairs medical center each received a set of eight 2-drug profiles, four 4-drug profiles, two 8-drug profiles, and one 16-drug profile. Each set of profiles contained a number of pairs of drugs rated by the Drug Therapy Screening System as producing an interaction of moderate or major importance. The subjects were given one hour to screen the profile for the potentially interacting pairs. The subjects detected only 66% of the interactions in the 2-drug profiles, 34% of the interactions in the 4-drug profiles, 20% of the interactions in the 8-drug profiles, and 17% of the interactions in the 16-drug profile. None of the subjects detected all interactions in the 8- or 16-drug profiles. Both true-positive and false-positive rates of identification decreased significantly as the number of drugs listed on the profile increased. This primarily reflected a reduced tendency to report the presence of drug interactions, but there was additional evidence that the accuracy of identification also declined. The number of years of pharmacy training was the only demographic characteristic highly correlated with accuracy. More years of pharmacy education seemed to improve the ability to detect drug interactions. However, none of the pharmacists or students was able to detect all potentially interacting pairs in a profile containing 8 or 16 drugs. Computerized drug interaction profiles should be used by pharmacists to ensure recognition of all potential drug interactions.

Drug Interactions↗
PubMed1995

Macrolides versus azalides: a drug interaction update.

OBJECTIVE: To describe the current drug interaction profiles for all approved and investigational macrolide and azalide antimicrobials, and to comment on the clinical impact of these interactions when appropriate. DATA SOURCES: MEDLINE was searched to identify all pertinent studies, review articles, and case reports from 1975 to 1995. When appropriate information was not available in the literature, data were obtained from the product manufacturers. STUDY SELECTION: All available data were reviewed to give an unbiased account of possible drug interactions. DATA EXTRACTION: Data for some of the interactions were not available from the literature, but were available from abstracts or from company-supplied materials. Although the data were not always entirely explicative, the best attempt was made to deliver the pertinent information that clinical practitioners would need to formulate practice opinions. When more in-depth information was supplied in the form of a review or study report, a thorough explanation of pertinent methodology was supplied. DATA SYNTHESIS: Since the introduction of erythromycin into clinical practice, there have been several clinically significant drug interactions identified throughout the literature associated with this drug. These interactions have been caused mostly by inhibition of the CYP3A subclass of hepatic enzymes, thereby decreasing the metabolism of any other agent given concurrently that is also cleared through this mechanism. With the development and marketing of several new macrolides, it was hoped that the drug interaction profile associated with this class would improve. This has been met with variable success. Although some of the extensions of the 14-membered ring macrolides have shown an incidence of interactions equal to that of erythromycin, others have shown improved profiles. In contrast, the 16-membered ring macrolides have demonstrated a much improved, though not absent, interaction profile. The most success in avoiding drug interactions through structure modification has been accomplished with the development of the azalide class, of which azithromycin is the first to be approved for marketing. This agent has to date produced none of the classic drug interactions that most macrolides have demonstrated in patient care. CONCLUSIONS: The introduction of new 14- and 16-membered ring macrolides appears to have had a variable effect in modifying the incidence of drug interactions associated with this class. Azithromycin, a member of the new azalide class, has to date produced fewer clinically significant interactions than other azalides with any agent that is cleared through the CYP3A system.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMed2003

No simple dependence between protein evolution rate and the number of protein-protein interactions: only the most prolific interactors tend to evolve slowly.

BACKGROUND: It has been suggested that rates of protein evolution are influenced, to a great extent, by the proportion of amino acid residues that are directly involved in protein function. In agreement with this hypothesis, recent work has shown a negative correlation between evolutionary rates and the number of protein-protein interactions. However, the extent to which the number of protein-protein interactions influences evolutionary rates remains unclear. Here, we address this question at several different levels of evolutionary relatedness. RESULTS: Manually curated data on the number of protein-protein interactions among Saccharomyces cerevisiae proteins was examined for possible correlation with evolutionary rates between S. cerevisiae and Schizosaccharomyces pombe orthologs. Only a very weak negative correlation between the number of interactions and evolutionary rate of a protein was observed. Furthermore, no relationship was found between a more general measure of the evolutionary conservation of S. cerevisiae proteins, based on the taxonomic distribution of their homologs, and the number of protein-protein interactions. However, when the proteins from yeast were assorted into discrete bins according to the number of interactions, it turned out that 6.5% of the proteins with the greatest number of interactions evolved, on average, significantly slower than the rest of the proteins. Comparisons were also performed using protein-protein interaction data obtained with high-throughput analysis of Helicobacter pylori proteins. No convincing relationship between the number of protein-protein interactions and evolutionary rates was detected, either for comparisons of orthologs from two completely sequenced H. pylori strains or for comparisons of H. pylori and Campylobacter jejuni orthologs, even when the proteins were classified into bins by the number of interactions. CONCLUSION: The currently available comparative-genomic data do not support the hypothesis that the evolutionary rates of the majority of proteins substantially depend on the number of protein-protein interactions they are involved in. However, a small fraction of yeast proteins with the largest number of interactions (the hubs of the interaction network) tend to evolve slower than the bulk of the proteins.

Bacterial Proteins↗
PubMed2004

A Drosophila protein-interaction map centered on cell-cycle regulators.

BACKGROUND: Maps depicting binary interactions between proteins can be powerful starting points for understanding biological systems. A proven technology for generating such maps is high-throughput yeast two-hybrid screening. In the most extensive screen to date, a Gal4-based two-hybrid system was used recently to detect over 20,000 interactions among Drosophila proteins. Although these data are a valuable resource for insights into protein networks, they cover only a fraction of the expected number of interactions. RESULTS: To complement the Gal4-based interaction data, we used the same set of Drosophila open reading frames to construct arrays for a LexA-based two-hybrid system. We screened the arrays using a novel pooled mating approach, initially focusing on proteins related to cell-cycle regulators. We detected 1,814 reproducible interactions among 488 proteins. The map includes a large number of novel interactions with potential biological significance. Informative regions of the map could be highlighted by searching for paralogous interactions and by clustering proteins on the basis of their interaction profiles. Surprisingly, only 28 interactions were found in common between the LexA- and Gal4-based screens, even though they had similar rates of true positives. CONCLUSIONS: The substantial number of new interactions discovered here supports the conclusion that previous interaction mapping studies were far from complete and that many more interactions remain to be found. Our results indicate that different two-hybrid systems and screening approaches applied to the same proteome can generate more comprehensive datasets with more cross-validated interactions. The cell-cycle map provides a guide for further defining important regulatory networks in Drosophila and other organisms.

Animals↗
PubMed2006

A pharmacoepidemiologic study of drug interactions in a Brazilian teaching hospital.

PURPOSE: Although drug-drug interactions constitute only a small proportion of adverse drug reactions, they are often predictable and therefore avoidable or manageable. There are few studies on drug-drug interactions from Brazil. This study aimed to assess the frequency of drug-drug interactions in prescriptions and their potential clinical significance in patients of a Brazilian teaching hospital. METHODS: From January to April 2004, a sample of 1785 drug prescriptions was drawn from a total of 11,250. Drug-drug interactions were identified by using Micromedex DrugReax System. Patients'records with major drug-drug interactions were reviewed by a pharmacist and a medical doctor looking for signs, symptoms, and lab tests that could indicate adverse drug reactions due to such interactions. RESULTS: From the 1785 prescriptions examined, 1089 (61%) were from the male adult ward. Patients' average age was 52.7 years (SD = 18.9; range, 12-98). The median number of drugs in each prescription was 7 (range, 2-26). At least 1 drug-drug interactions was present in 887 (49.7%) prescriptions. Regarding the severity of the clinical result, the interactions were classified as minor (20; 2.3%), moderate (184; 20.7%), major (30; 3.4%), and undetermined because of an incidence of more than 1 interaction in a single patient (653; 73.6%). From the 30 patients with major interactions, 17 (56.7%) presented adverse drug reactions induced by exposure to a major drug-drug interaction. CONCLUSIONS: Patients did suffer adverse drug reactions from major drug-drug interactions. Many physicians may be unaware of drug-drug interactions. Education, computerized prescribing systems and drug information, collaborative drug selection, and pharmaceutical care are strongly encouraged for physicians and pharmacists.

Adolescent↗
PubMed2000

Antiarrhythmic agents: drug interactions of clinical significance.

The management of cardiac arrhythmias has grown more complex in recent years. Despite the recent focus on nonpharmacological therapy, most clinical arrhythmias are treated with existing antiarrhythmics. Because of the narrow therapeutic index of antiarrhythmic agents, potential drug interactions with other medications are of major clinical importance. As most antiarrhythmics are metabolised via the cytochrome P450 enzyme system, pharmacokinetic interactions constitute the majority of clinically significant interactions seen with these agents. Antiarrhythmics may be substrates, inducers or inhibitors of cytochrome P450 enzymes, and many of these metabolic interactions have been characterised. However, many potential interactions have not, and knowledge of how antiarrhythmic agents are metabolised by the cytochrome P450 enzyme system may allow clinicians to predict potential interactions. Drug interactions with Vaughn-Williams Class II (beta-blockers) and Class IV (calcium antagonists) agents have previously been reviewed and are not discussed here. Class I agents, which primarily block fast sodium channels and slow conduction velocity, include quinidine, procainamide, disopyramide, lidocaine (lignocaine), mexiletine, flecainide and propafenone. All of these agents except procainamide are metabolised via the cytochrome P450 system and are involved in a number of drug-drug interactions, including over 20 different interactions with quinidine. Quinidine has been observed to inhibit the metabolism of digoxin, tricyclic antidepressants and codeine. Furthermore, cimetidine, azole antifungals and calcium antagonists can significantly inhibit the metabolism of quinidine. Procainamide is excreted via active tubular secretion, which may be inhibited by cimetidine and trimethoprim. Other Class I agents may affect the disposition of warfarin, theophylline and tricyclic antidepressants. Many of these interactions can significantly affect efficacy and/or toxicity. Of the Class III antiarrhythmics, amiodarone is involved in a significant number of interactions since it is a potent inhibitor of several cytochrome P450 enzymes. It can significantly impair the metabolism of digoxin, theophylline and warfarin. Dosages of digoxin and warfarin should empirically be decreased by one-half when amiodarone therapy is added. In addition to pharmacokinetic interactions, many reports describe the use of antiarrhythmic drug combinations for the treatment of arrhythmias. By combining antiarrhythmic drugs and utilising additive electrophysiological/pharmacodynamic effects, antiarrhythmic efficacy may be improved and toxicity reduced. As medication regimens grow more complex with the aging population, knowledge of existing and potential drug-drug interactions becomes vital for clinicians to optimise drug therapy for every patient.

Animals↗
PubMed2001

Clinical-pharmacological strategies to assess drug interaction potential during drug development.

Drug interactions in patients receiving multiple drug regimens are a constant concern for the clinician. With the increased availability of new drugs and their concomitant use with other drugs, there has been a rise in the potential for adverse drug interactions as demonstrated by the recent withdrawals of newly marketed drugs because of unacceptable interaction profiles. Therefore, the interaction potential of a new compound has to be assessed in detail, starting with preclinical in vitro and in vivo studies at candidate selection and continuously followed up through preclinical and clinical development. Since formal in vivo studies of all possible drug interactions are neither practicable nor suggestive, a careful selection of a limited number of drug combinations to be investigated in vivo during the development phase is indicated. Based on knowledge of pharmacokinetic and biopharmaceutical properties, a well balanced link between in vitro investigations and carefully selected in vivo interaction studies allows full assessment of the potential of a new drug to cause clinically relevant pharmacokinetic drug-drug interactions, prediction of a lack of interactions and derivation of the proper dose recommendations. Clinical pharmacology plays a number of key roles within the process of collecting information on drug interactions during preclinical and clinical development: addressing issues and/or favourable properties to be expected, thus contributing to the scientific assessment of development potential; setting up a rational in vivo drug-drug interaction programme; performing early mechanistic studies to link in vitro with in vivo information (employing 'cocktail' approaches if possible); reviewing co-medication sections for clinical trials; and conducting labelling-oriented interaction studies, after proof of concept. The fact that interactions can occur between various active substances should by itself be a conclusive argument against unnecessary polypharmacy. Prescribing fewer drugs on a rational basis can reduce the risk of adverse effects secondary to drug interactions and may help to improve the quality of drug treatment and to save costs.

Drug Approval↗
PubMed2006

Pharmacokinetic drug interaction profiles of proton pump inhibitors.

Proton pump inhibitors are used extensively for the treatment of gastric acid-related disorders because they produce a greater degree and longer duration of gastric acid suppression and, thus, better healing rates, than histamine H(2) receptor antagonists. The need for long-term treatment of these disorders raises the potential for clinically significant drug interactions in patients receiving proton pump inhibitors and other medications. Therefore, it is important to understand the mechanisms for drug interactions in this setting. Proton pump inhibitors can modify the intragastric release of other drugs from their dosage forms by elevating pH (e.g. reducing the antifungal activity of ketoconazole). Proton pump inhibitors also influence drug absorption and metabolism by interacting with adenosine triphosphate-dependent P-glycoprotein (e.g. inhibiting digoxin efflux) or with the cytochrome P450 (CYP) enzyme system (e.g. decreasing simvastatin metabolism), thereby affecting both intestinal first-pass metabolism and hepatic clearance. Although interactions based on the change of gastric pH are a group-specific effect and thus may occur with all proton pump inhibitors, individual proton pump inhibitors differ in their propensities to interact with other drugs and the extent to which their interaction profiles have been defined. The interaction profiles of omeprazole and pantoprazole have been studied most extensively. A number of studies have shown that omeprazole carries a considerable potential for drug interactions, since it has a high affinity for CYP2C19 and a somewhat lower affinity for CYP3A4. In contrast, pantoprazole appears to have lower potential for interactions with other medications. Although the interaction profiles of esomeprazole, lansoprazole and rabeprazole have been less extensively investigated, evidence suggests that lansoprazole and rabeprazole seem to have a weaker potential for interactions than omeprazole. Although only a few drug interactions involving proton pump inhibitors have been shown to be of clinical significance, the potential for drug interactions should be taken into account when choosing a therapy for gastric acid-related disorders, especially for elderly patients in whom polypharmacy is common, or in those receiving a concomitant medication with a narrow therapeutic index.

Anti-Ulcer Agents↗
PubMed2003

Drug interactions of clinical significance for the dermatologist: recognition and avoidance.

While it would be impossible for any dermatologist to remember all potential drug interactions, knowledge of the mechanisms of drug interactions can help reduce the risk of serious adverse outcomes. Most drugs are associated with interactions but the majority do not produce significant outcomes. Dealing with drug interactions is a challenge in all clinical practice, including dermatology. New information continues to appear, and dermatologists need to know about the drugs they use.This article focuses on the mechanisms of drug interactions. In particular, the life of a drug in terms of absorption, distribution, metabolism and excretion are reviewed with the focus on points of importance and relevance to drug interactions. The most clinically important drug interactions in dermatological practice are caused by alterations in drug metabolism. The contributions of P-glycoprotein, pharmacogenetic variation and genetic polymorphisms to drug interactions are highlighted, and the best evidence for drug interactions involving drug classes relevant to the dermatologist is presented. Since the initial evidence for clinically relevant drug interactions comes from case reports, prescribing physicians can have a major role in collating information on interactions. By understanding the mechanisms behind drug interactions and staying alert for toxicities, we can help make drug therapy safer and reduce the fear of drug interactions.

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